Deep learning-based dynamic ventilatory threshold estimation from electrocardiograms.

BACKGROUND AND OBJECTIVE: The ventilatory threshold (VT) marks the transition from aerobic to anaerobic metabolism and is used to assess cardiorespiratory endurance. A conventional way to assess VT is cardiopulmonary exercise testing, which requires a gas analyzer. Another method for measuring VT involves calculating the heart rate variability (HRV) from an electrocardiogram (ECG) by computing the variability of heartbeats. However, the HRV method has some limitations. ECGs should be recorded for at least 5 minutes to calculate the HRV, and the result may depend on the utilized ECG preprocessing algorithms. METHODS: To overcome these problems, we developed a deep learning-based model consisting of long short-term memory (LSTM) and convolutional neural network (CNN) for a lead II ECG. Variables reflecting subjects' physical characteristics, as well as ECG signals, were input into the model to estimate VT. We applied joint optimization to the CNN layers to generate an informative latent space, which was fed to the LSTM layers. The model was trained and evaluated on two datasets, one from the Bruce protocol and the other from a protocol including multiple tasks (MT). RESULTS: Acceptable performances (mean and 95% CI) were obtained on the datasets from the Bruce protocol (-0.28[-1.91,1.34] ml/min/kg) and the MT protocol (0.07[-3.14,3.28] ml/min/kg) regarding the differences between the predictions and labels. The coefficient of determination, Pearson correlation coefficient, and root mean square error were 0.84, 0.93, and 0.868 for the Bruce protocol and 0.73, 0.97, and 3.373 for the MT protocol, respectively. CONCLUSIONS: The results indicated that it is possible for the proposed model to simultaneously assess VT with the inputs of successive ECGs. In addition, from ablation studies concerning the physical variables and the joint optimization process, it was demonstrated that their use could boost the VT assessment performance of the model. The proposed model enables dynamic VT estimation with ECGs, which could help with managing cardiorespiratory fitness in daily life and cardiovascular rehabilitation in patients.

IL-6/JAK2/STAT3 axis mediates neuropathic pain by regulating astrocyte and microglia activation after spinal cord injury.

After spinal cord injury (SCI), the control of activated glial cells such as microglia and astrocytes has emerged as a promising strategy for neuropathic pain management. However, signaling mechanism involved in glial activation in the process of neuropathic pain development and maintenance after SCI is not well elucidated. In this study, we investigated the potential role and mechanism of the JAK2/STAT3 pathway associated with glial cell activation in chronic neuropathic pain development and maintenance after SCI. One month after contusive SCI, the activation of JAK2/STAT3 pathway was markedly upregulated in both microglia and astrocyte in nociceptive processing regions of the lumbar spinal cord. In addition, both mechanical allodynia and thermal hyperalgesia was significantly inhibited by a JAK2 inhibitor, AG490. In particular, AG490 treatment inhibited both microglial and astrocyte activation in the lumbar (L) 4-5 dorsal horn and significantly decreased levels of p-p38MAPK, p-ERK and p-JNK, which are known to be activated in microglia (p-p38MAPK and p-ERK) and astrocyte (p-JNK). Experiments using primary cell cultures also revealed that the JAK2/STAT3 pathway promoted microglia and astrocyte activation after lipopolysaccharide stimulation. Furthermore, JAK2/STAT3 signaling and pain behaviors were significantly attenuated when the rats were treated with anti-IL-6 antibody. Finally, minocycline, a tetracycline antibiotic, inhibited IL-6/JAK2/STAT3 signaling pathway in activated glial cells and restored nociceptive thresholds and the hyperresponsiveness of dorsal neurons. These results suggest an important role of the IL-6/JAK2/STAT3 pathway in the activation of microglia and astrocytes and in the maintenance of chronic below-level pain after SCI.

Parathyroid gland detection using an intraoperative autofluorescence handheld imager - early feasibility study.

Introduction: Parathyroid glands may be compromised during thyroid surgery which can lead to hypoparathyroidism and hypocalcemia. Identifying the parathyroid glands relies on the surgeon's experience and the only way to confirm their presence was through tissue biopsy. Near infrared autofluorescence technology offers an opportunity for real-time, non-invasive identification of the parathyroid glands. Methods: We used a new research prototype (hANDY-I) developed by Optosurgical, LLC. It offers coaxial excitation light and a dual-Red Green Blue/Near Infrared sensor that guides anatomical landmarks and can aid in identification of parathyroid glands by showing a combined autofluorescence and colored image simultaneously. Results: We tested the imager during 23 thyroid surgery cases, where initial clinical feasibility data showed that out of 75 parathyroid glands inspected, 71 showed strong autofluorescence signal and were correctly identified (95% accuracy) by the imager. Conclusions: The hANDY-I prototype demonstrated promising results in this feasibility study by aiding in real-time visualization of the parathyroid glands. However, further testing by conducting randomized clinical trials with a bigger sample size is required to study the effect on levels of hypoparathyroidism and hypocalcemia.

Classification of gait phases based on a machine learning approach using muscle synergy.

The accurate detection of the gait phase is crucial for monitoring and diagnosing neurological and musculoskeletal disorders and for the precise control of lower limb assistive devices. In studying locomotion mode identification and rehabilitation of neurological disorders, the concept of modular organization, which involves the co-activation of muscle groups to generate various motor behaviors, has proven to be useful. This study aimed to investigate whether muscle synergy features could provide a more accurate and robust classification of gait events compared to traditional features such as time-domain and wavelet features. For this purpose, eight healthy individuals participated in this study, and wireless electromyography sensors were attached to four muscles in each lower extremity to measure electromyography (EMG) signals during walking. EMG signals were segmented and labeled as 2-class (stance and swing) and 3-class (weight acceptance, single limb support, and limb advancement) gait phases. Non-negative matrix factorization (NNMF) was used to identify specific muscle groups that contribute to gait and to provide an analysis of the functional organization of the movement system. Gait phases were classified using four different machine learning algorithms: decision tree (DT), k-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN). The results showed that the muscle synergy features had a better classification accuracy than the other EMG features. This finding supported the hypothesis that muscle synergy enables accurate gait phase classification. Overall, the study presents a novel approach to gait analysis and highlights the potential of muscle synergy as a tool for gait phase detection.

Targeted photothrombotic subcortical small vessel occlusion using in vivo real-time fiber bundle endomicroscopy in mice.

The development of an accurate subcortical small vessel occlusion model for pathophysiological studies of subcortical ischemic stroke is still insignificant. In this study, in vivo real-time fiber bundle endomicroscopy (FBEµ) was applied to develop subcortical photothrombotic small vessel occlusion model in mice with minimal invasiveness. Our FBFµ system made it possible to precisely target specific blood vessels in deep brain and simultaneously observe the clot formation and blood flow blockage inside the target blood vessel during photochemical reactions. A fiber bundle probe was directly inserted into the anterior pretectal nucleus of the thalamus in brain of live mice to induce a targeted occlusion in small vessels. Then, targeted photothrombosis was performed using a patterned laser, observing the process through the dual-color fluorescence imaging. On day one post occlusion, infarct lesions are measured using TTC staining and post hoc histology. The results show that FBEµ applied to targeted photothrombosis can successfully generate a subcortical small vessel occlusion model for lacunar stroke.

Perspective: Present and Future of Virtual Reality for Neurological Disorders.

Since the emergence of Virtual Reality technology, it has been adopted in the field of neurology. While Virtual Reality has contributed to various rehabilitation approaches, its potential advantages, especially in diagnosis, have not yet been fully utilized. Moreover, new tides of the Metaverse are approaching rapidly, which will again boost public and research interest and the importance of immersive Virtual Reality technology. Nevertheless, accessibility to such technology for people with neurological disorders has been critically underexplored. Through this perspective paper, we will briefly look over the current state of the technology in neurological studies and then propose future research directions, which hopefully facilitate beneficial Virtual Reality studies on a wider range of topics in neurology.

Single-lead ECG based autonomic nervous system assessment for meditation monitoring.

We propose a single-lead ECG-based heart rate variability (HRV) analysis algorithm to quantify autonomic nervous system activity during meditation. Respiratory sinus arrhythmia (RSA) induced by breathing is a dominant component of HRV, but its frequency depends on an individual's breathing speed. To address this RSA issue, we designed a novel HRV tachogram decomposition algorithm and new HRV indices. The proposed method was validated by using a simulation, and applied to our experimental (mindfulness meditation) data and the WESAD open-source data. During meditation, our proposed HRV indices related to vagal and sympathetic tones were significantly increased (p < 0.000005) and decreased (p < 0.000005), respectively. These results were consistent with self-reports and experimental protocols, and identified parasympathetic activation and sympathetic inhibition during meditation. In conclusion, the proposed method successfully assessed autonomic nervous system activity during meditation when respiration influences disrupted classical HRV. The proposed method can be considered a reliable approach to quantify autonomic nervous system activity.

Targeted thrombolysis by magnetoacoustic particles in photothrombotic stroke model.

BACKGROUND: Recombinant tissue plasminogen activator (rtPA) has a short half-life, and additional hemorrhagic transformation (HT) can occur when treatment is delayed. Here, we report the design and thrombolytic performance of 3 [Formula: see text]m discoidal polymeric particles loaded with rtPA and superparamagnetic iron oxide nanoparticles (SPIONs), referred to as rmDPPs, to address the HT issues of rtPA. METHODS: The rmDPPs consisted of a biodegradable polymeric matrix, rtPA, and SPIONs and were synthesized via a top-down fabrication. RESULTS: The rmDPPs could be concentrated at the target site with magnetic attraction, and then the rtPA could be released under acoustic stimulus. Therefore, we named that the particles had magnetoacoustic properties. For the in vitro blood clot lysis, the rmDPPs with magnetoacoustic stimuli could not enhance the lytic potential compared to the rmDPPs without stimulation. Furthermore, although the reduction of the infarcts in vivo was observed along with the magnetoacoustic stimuli in the rmDPPs, more enhancement was not achieved in comparison with the rtPA. A notable advantage of rmDPPs was shown in delayed administration of rmDPPs at poststroke. The late treatment of rmDPPs with magnetoacoustic stimuli could reduce the infarcts and lead to no additional HT issues, while rtPA alone could not show any favorable prognosis. CONCLUSION: The rmDPPs may be advantageous in delayed treatment of thrombotic patients.

Cavitation-assisted sonothrombolysis by asymmetrical nanostars for accelerated thrombolysis.

Sonothrombolysis with recombinant tissue plasminogen activator (rtPA) and microbubbles has been widely studied to enhance thrombolytic potential. Here, we report different sonothrombolysis strategy in nanoparticles using microbubbles cavitation. We found that different particles in shape exhibited different reactivity toward the cavitation, leading to a distinct sonothrombolytic potential. Two different gold nanoparticles in shape were functionalized with the rtPA: rtPA-functionalized gold nanospheres (NPt) and gold nanostars (NSt). NPt could not accelerate the thrombolytic potential with a sole acoustic stimulus. Importantly, NSt enhanced the potential with acoustic stimulus and microbubble-mediated cavitation, while NPt were not reactive to cavitation. Coadministration of NSt and microbubbles resulted in a dramatic reduction of the infarcts in a photothrombotic model and recovery in the cerebral blood flow. Given the synergistic effect and in vivo feasibility of this strategy, cavitation-assisted sonothrombolysis by asymmetrical NSt might be useful for treating acute ischemic stroke.

Cooperative Surface Passivation and Hierarchical Structuring of Zeolite Beta Catalysts.

We report a method to prepare core-shell zeolite beta (*BEA) with an aluminous core and an epitaxial Si-rich shell. This method capitalizes on the inherent defects in *BEA crystals to simultaneously passivate acid sites on external surfaces and increase intracrystalline mesoporosity through facile post-hydrothermal synthesis modification in alkaline media. This process creates more hydrophobic materials by reducing silanol defects and enriching the shell in silica via a combination of dealumination and the relocation of silica from the core to the shell during intracrystalline mesopore formation. The catalytic consequences of *BEA core-shells relative to conventional analogues were tested using the biomass conversion of levulinic acid and n-butanol to n-butyl levulinate as a benchmark reaction. Our findings reveal that siliceous shells and intracrystalline mesopores synergistically enhance the performance of *BEA catalysts.

Heart Rate Variability as a Potential Indicator of Cancer Pain in a Mouse Model of Peritoneal Metastasis.

Heart rate variability (HRV) is closely related to changes in the autonomic nervous system (ANS) associated with stress and pain. In this study, we investigated whether HRV could be used to assess cancer pain in mice with peritoneal metastases. At 12 days after cancer induction, positive indicators of pain such as physiological characteristics, appearance, posture, and activity were observed, and time- and frequency-domain HRV parameters such as mean R-R interval, square root of the mean squared differences of successive R-R intervals, and percentage of successive R-R interval differences greater than 5 ms, low frequency (LF), high frequency (HF), and ratio of LF and HF power, were found to be significantly decreased. These parameters returned to normal after analgesic administration. Our results indicate that overall ANS activity was decreased by cancer pain and that HRV could be a useful tool for assessing pain.

Catalytic Reactions on Pd-Au Bimetallic Model Catalysts.

ConspectusThe enhanced catalytic activity of Pd-Au catalysts originates from ensemble effects related to the local composition of Pd and Au. The study of Pd-Au planar model catalysts in an ultrahigh vacuum (UHV) environment allows the observation of molecular level catalytic reactions between the Pd-Au surface and target molecules. Recently, there has been progress in understanding the behavior of simple molecules (H2, O2, CO, etc.) employing UHV surface science techniques, the results of which can be applied not only to heterogeneous catalysis but also to electro- and photochemical catalysis.Employing UHV methods in the investigation of Pd-Au model catalysts has shown that single Pd atoms can dissociatively adsorb H2 molecules. The recombinative desorption temperature of H2 varies with Pd ensemble size, which allows the use of H2 as a probe molecule for quantifying surface composition. In particular, H2 desorption from Pd-Au interface sites (or small Pd ensembles) is observed from 150-300 K, which is between the H2 desorption temperature from pure Au (∼110 K) and Pd (∼350 K) surfaces. When the Pd ensembles are large enough to form Pd(111)-like islands, H2 desorption occurs from 300-400 K, as with pure Pd surfaces. The different H2 desorption behavior, which depends on Pd ensemble size, has also been applied to the analysis of dehydrogenation mechanisms for potential liquid storage mediums for H2, namely formic acid and ethanol. In both cases, the Pd-Au interface is the main reaction site for generating H2 from formic acid and ethanol with less overall decomposition of the two molecules (compared to pure Pd).The chemistry behind O2 activation has also been informed through the control of Pd ensembles on a gold model catalyst for acetaldehyde and ethanol oxidation reactions under UHV conditions. O2 molecules molecularly adsorbed on continuous Pd clusters can be dissociated into O adatoms above 180 K. This O2 activation process is improved by coadsorbed H2O molecules. It is also possible to directly (through a precursor mechanism) introduce O adatoms on the Pd-Au surface by exposure to O2 at 300 K. The quantity of dissociatively adsorbed O adatoms is proportional to the Pd coverage. However, the O adatoms are more reactive on a less Pd covered surface, especially at the Pd-Au interface sites, which can initiate CO oxidation at temperatures as low as 140 K. Acetaldehyde molecules can be selectively oxidized to acetic acid on the Pd-Au surface with O adatoms, in which the selectivity toward acetic acid originates from preventing the decarboxylation of acetate species. Moreover, the O adatoms on the Pd-Au surface accelerate ethanol dehydrogenation, which causes the increase in acetaldehyde production. Hydrogen is continuously abstracted from the formed acetaldehyde and remaining ethanol molecules, and they ultimately combine as ethyl acetate on the Pd-Au surface.Using Pd-Au model catalysts under UHV conditions allows the discovery of molecular level mechanistic details regarding the catalytic behavior of H and O adatoms with other molecules. We also expect that these findings will be applicable regarding other chemistry on Pd-Au catalysts.

Prediction of Lower Extremity Multi-Joint Angles during Overground Walking by Using a Single IMU with a Low Frequency Based on an LSTM Recurrent Neural Network.

The joint angle during gait is an important indicator, such as injury risk index, rehabilitation status evaluation, etc. To analyze gait, inertial measurement unit (IMU) sensors have been used in studies and continuously developed; however, they are difficult to utilize in daily life because of the inconvenience of having to attach multiple sensors together and the difficulty of long-term use due to the battery consumption required for high data sampling rates. To overcome these problems, this study propose a multi-joint angle estimation method based on a long short-term memory (LSTM) recurrent neural network with a single low-frequency (23 Hz) IMU sensor. IMU sensor data attached to the lateral shank were measured during overground walking at a self-selected speed for 30 healthy young persons. The results show a comparatively good accuracy level, similar to previous studies using high-frequency IMU sensors. Compared to the reference results obtained from the motion capture system, the estimated angle coefficient of determination (R2) is greater than 0.74, and the root mean square error and normalized root mean square error (NRMSE) are less than 7° and 9.87%, respectively. The knee joint showed the best estimation performance in terms of the NRMSE and R2 among the hip, knee, and ankle joints.

Current Progress and Future Directions in Gas-Phase Metal-Organic Framework Thin-Film Growth.

Deposition of materials as a thin film is important for various applications, such as sensors, microelectronic devices, and membranes. There have been breakthroughs in gas-phase metal-organic framework (MOF) thin-film growth, which is more applicable to micro- and nanofabrication processes and also less harmful to the environment than solvent-based methods. Three different types of gas-phase MOF thin film deposition methods have been developed using chemical vapor deposition (CVD), atomic layer deposition (ALD), and physical vapor deposition (PVD)-CVD combined techniques. The CVD-based method basically converts metal oxide layers into MOF thin films by exposing the surface to ligand vapor. The ALD-based method allows growing MOF thin films following layer-by-layer (LBL) growth by sequentially exposing gas-phase metal and ligand precursors. The PVD-CVD method uses PVD for metal deposition and CVD for ligand deposition, which is similar to LBL growth. These gas-phase growth methods can broaden the use of MOFs in diverse areas. Herein, the current progress of gas-phase MOF thin film growth is discussed and future directions suggested.

Ultrasound Stimulation Increases Neurite Regeneration in Injured Dorsal Root Ganglion Neurons through Mammalian Target of Rapamycin Activation.

Ultrasound stimulation (US) is reported to be a safe and useful technology for improving injured nerve regeneration. However, the intracellular mechanisms underlying its stimulatory effects are only partially understood. Mammalian target of rapamycin (mTOR) signaling is involved in neuronal survival and axonal outgrowth. In this study, we investigated the effect of US on regeneration of injured dorsal root ganglion (DRG) neurons and activation of the mTOR pathway. We showed that US significantly increased neurite regeneration and enhanced mTOR activation. Moreover, the expression of growth-associated protein-43 (GAP-43), a crucial factor for axonal outgrowth and regeneration in neurons, was significantly increased by US. These data suggest that US-induced neurite regeneration is mediated by upregulation of mTOR activity, which promotes the regeneration of injured DRG neurons.

Hydrogen desorption from the surface and subsurface of cobalt.

The influence of coverage on the diffusion of hydrogen into the subsurface of cobalt was studied using density functional theory (DFT) and temperature programmed desorption (TPD). DFT calculations show that as the hydrogen coverage on Co(0001) increases, the barrier for hydrogen diffusion into the bulk decreases by 20%. Additionally, subsurface hydrogen on a hydrogen covered surface was found to be more stable when compared to a clean cobalt surface. To test these theoretical findings experimentally, excited hydrogen was used in an ultra-high vacuum environment to access higher hydrogen coverages. Our TPD studies showed that at high hydrogen coverages, a sharp low temperature feature appeared, indicating the stabilization of subsurface hydrogen. Further DFT calculations indicate that this sharp low temperature feature results from associative hydrogen desorption from a hydrogen saturated surface with a population of subsurface hydrogen. Microkinetic modelling was used to model the TPD spectra for hydrogen desporption from cobalt with and without subsurface hydrogen, showing reasonable agreement with experiment.

Low temperature dissociation of CO on manganese promoted cobalt(poly).

We observe that metallic manganese alloyed with polycrystalline cobalt promotes the dissociation of CO. Increasing coverages of Mn on Co facillitate stronger molecular CO binding energies and stronger C(ad) + O(ad) binding energies. These findings show the role of metallic manganese in promoting model Co Fischer-Tropsch catalysis.

Ultrasonic Neuromodulation via Astrocytic TRPA1.

Low-intensity, low-frequency ultrasound (LILFU) is the next-generation, non-invasive brain stimulation technology for treating various neurological and psychiatric disorders. However, the underlying cellular and molecular mechanism of LILFU-induced neuromodulation has remained unknown. Here, we report that LILFU-induced neuromodulation is initiated by opening of TRPA1 channels in astrocytes. The Ca2+ entry through TRPA1 causes a release of gliotransmitters including glutamate through Best1 channels in astrocytes. The released glutamate activates NMDA receptors in neighboring neurons to elicit action potential firing. Our results reveal an unprecedented mechanism of LILFU-induced neuromodulation, involving TRPA1 as a unique sensor for LILFU and glutamate-releasing Best1 as a mediator of glia-neuron interaction. These discoveries should prove to be useful for optimization of human brain stimulation and ultrasonogenetic manipulations of TRPA1.

Linear feature projection-based real-time decoding of limb state from dorsal root ganglion recordings.

Proprioceptive afferent activities recorded by a multichannel microelectrode have been used to decode limb movements to provide sensory feedback signals for closed-loop control in a functional electrical stimulation (FES) system. However, analyzing the high dimensionality of neural activity is one of the major challenges in real-time applications. This paper proposes a linear feature projection method for the real-time decoding of ankle and knee joint angles. Single-unit activity was extracted as a feature vector from proprioceptive afferent signals that were recorded from the L7 dorsal root ganglion during passive movements of ankle and knee joints. The dimensionality of this feature vector was then reduced using a linear feature projection composed of projection pursuit and negentropy maximization (PP/NEM). Finally, a time-delayed Kalman filter was used to estimate the ankle and knee joint angles. The PP/NEM approach had a better decoding performance than did other feature projection methods, and all processes were completed within the real-time constraints. These results suggested that the proposed method could be a useful decoding method to provide real-time feedback signals in closed-loop FES systems.